These days, acquiring data is more popular than ever before. It ranges from commercial applications, e.g. super market transactions, stock market recordings to scientific data collections, such as genome analysis, astronomy and weather observations or nuclear experiments, to name a few. Hence, data appears in many forms and grows explosively.
Nowadays, companies and organizations generate terabytes of event data on a daily basis. For instance, state of the art machines, such as constructing or hoisting machines, employ a data logging software on its PLC (Programmable Logic Controller), that records event data generated by running programs and sensors. This data enables skilled persons to monitor the status of the machine. Hence, the ability to store and monitor event data records on a permanent basis has become a necessity for detecting malicious behaviour, hazard states and other security issues.
Due to its magnitude and to its complex nature, the analysis of data is no longer feasible by a human being. Therefore, it is desirable to provide methods for automatically monitoring and analyzing the collected event data in order to observe performance degradation or technical issues of the monitored machines.
At the moment, several methods and algorithms exist that detect or mine interesting relations, patterns and hidden knowledge in our data. The formal term for this process of extracting interesting, non-trivial, implicit, previously unknown and potentially useful information or patterns from large information repositories, e.g. a database, is denoted as data mining.
Data mining forms the core process of Knowledge Discovery in Database (KDD). KDD consists of three consecutively applied processes. A first step is called pre-processing and implements data cleansing, integration, selection and transformation methods. Then, the main process, i.e. data mining, applies different algorithms to detect implicit knowledge. Finally, the post-processing step evaluates the mining results according to the user given constraints and requirements.
In case the data has a temporal or sequential nature, i.e. the order in which the elements appear is relevant, a set of special algorithms is designed to detect sequential patterns.
Many known methods for monitoring machines only involve mining of a single dimension event data making it difficult to find common patterns in a selection of machines or similar patterns for machines of a product family.
It is one object of the present disclosure to improve and extend existing methods for monitoring machines, in particular to adapt these methods for being applicable to machine fleets with different but similar machine types. It is a further object of the present disclosure to provide a system for centrally monitoring and diagnosing machine events and machine states in order to improve customer service.
In accordance with the present disclosure, one object is solved by a method with the features of claim 1.
Accordingly, there is provided a method for monitoring at least one machine, in particular a construction or hoisting machine. Preferably, the method is for monitoring a plurality of machines, such as a machine fleet, having identical and/or at least two different but similar machines. All or at least a part of said machines has data logging means for providing event data. For instance, said machine, such as constructing or hoisting machine, employs a data logging software on its machine control that records event data generated by executed applications, functions and programs thereon and/or provided by sensors as measuring results. The machine control can be a Programmable Logic Controller (PLC) executing a data logging software.
Preferably at least one machine can be a port crane or a deck crane.
The inventive method comprises the steps of transferring event data from at least one of the machines to a central processor, mining a multi-dimensional sequential pattern within said transferred event data wherein at least one dimensional attribute holds information indicating said event data generating machine or at least one machine property, in particular the product family of the event data generating machine, and matching said mined multi-dimensional sequential pattern with patterns stored in a central pattern database.
The central processing is part of a central computer, which may be a portable or a laptop computer or a mainframe or a network server or another computer configuration.
At least between one machine and said central processing unit a communicative connection is permanently established or can be temporarily established for data transfer. The connection can be based on wireline or wireless connection using an own or present network, in particular a mobile communication network.
The multi-dimensional attributes hold the information indicating said event data generating machine or at least one machine property. For instance at least one attribute contains information on the product family of the pattern generating machine. Mining multi-dimensional sequential patterns enables the central processor to detect patterns in a selection of machines and/or similar patterns within a product family and/or different product families having similar patterns.
At that point, the inventive method is not only applicable for monitoring identical machines. By adding at least one attribute into the sequential patterns it is possible to describe the type and properties of machines supporting said sequential patterns. For example, the sequence patterns hold information about the event itself. Adding more attributes to the multi-dimensional patterns helps to provide additional information about the machine itself, for example at least one attribute describes the machine type, in particular a constructing or hoisting machine, at least one attribute identifies the membership to a special machine family, at least one attribute references the pattern to a special machine part, such as the machine drive, hydraulic system, mechanical parts, hoisting gear, etc.
By means of multi-dimensional sequential pattern mining all relevant relations/patterns within the data can be detected. These patterns present a salient part of the data that needs to be analysed.
The identified patterns are compared to a central pattern databank. Said databank includes a number of known patterns. If the identified pattern matches a known pattern according reactions can be automatically executed. Therefore, it is possible to identify the correlation between the patterns and the hardware of the machine generating event data and thus provide an automatic approach to preventive maintenance.
In an advantageous aspect of the present disclosure a mined multi-dimensional pattern is stored in said database in case it does not match a known stored pattern. This offers the opportunity to upgrade and enlarge said database during real-time processing.
In accordance with another advantageous aspect of the present disclosure, stored patterns in said database are classified, in particular with respect to their severity for the machine operation. For instance, stored patterns are classified in severe patterns characterizing the occurrence of an important and abnormal event which could lead to critical degradation of the machine or operating persons.
Stored patterns can also be classified into less severe patters which do not imply an imminent danger for the machine and operating staff but necessitating appropriate actions in the future.
Further, it can be possible to classify the stored pattern into uncritical patterns characterizing the occurrence of ordinary or regularly recurring events which do not imply any degradation to the machine or operating staff.
Of course, the present disclosure is not restricted to the mentioned categories. It is obvious that an undefined number of categories is possible, allowing a smoother graduation of the pattern classification.
Furthermore, patterns rated as severe can be stored as blacklist patterns and all others can be stored as whitelist patterns.
To provide a very flexible pattern database it might be useful to enable manual insertion of patterns into said pattern database. Several patterns might be explored during development of said machines. Therefore, these patterns should be entered manually into the database during real-time processes.
The general form of event data logged by at least one machine and transferred to said central processing unit consist advantageously of at least one of the following information fields Event ID, Timestamp, Type of Event and Boolean values or values cohering with a very event.
The Event ID can be a unique number referencing an entry of event data into the log file. In principle, the event ID is a consecutive number for the temporally occurring events.
The timestamp gives the exact time of a single event and the “Type of Event” field gives a short description of the occurred and logged event.
An optional field containing a Boolean value can be added for providing additional status information about the event. Such Boolean value might be a flag as “Is event First After Boot” with values “True” or “False” indicating that said event occurred right after a machine restart. The Boolean value also might give information of whether this event is the first one since booting the machine.
Further, said event data can also contain a value field wherein the according value coheres with the event.
A single event record might hold information on the event that occurred on the machine in question at the date, given by the timestamp, plus values describing the event in more detail, e.g. at a special timestamp, the Load Spectrum Counter (LSC) of a hoisting machine were read out, plus the actual values of the LSC. Hence, the event data shows a history of states the machine was in.
For further prosecution of the logged and transferred data it is transformed to a sequence database. Basically, an event data, as described above, simply represents one long sequence. Each occurred event stands for a single item of said sequence. Some items or rather events might be combined to an itemset or eventset. A sequence database consists of several sequences wherein each row of said sequence database can represent a sequence.
A number of several subsequences is obtained by splitting said long sequence, basically all occurred and logged event data. Splitting the long sequence of event data into at least two subsequences representing single entries of the sequence database wherein each subsequence may form a row of said sequence database.
Said data conversion of event data into a sequence database is applied to prepare the recorded event data for the subsequent process of data mining. A certain data structure such as a sequence database can be convenient for executing data mining algorithm.
The splitting can be triggered by logical interruptions, such as a machine restart or a restart of the respective machine or controller parts. Alternatively or additionally, the splitting can be triggered by causal interruptions, in particular a time interval with no occurring events wherein the time interval exceeds a given time threshold.
Said sequence database is referred to as a multi-dimensional sequence database when additional attributes are added to said sequences stored in said database. One possibility for adding multi-dimensional attributes is to form a multi-dimensional database wherein each row represents a multidimensional sequence which consists of the dimensional information of the very sequence or rather subsequence.
Alternatively, it is possible to embed the additional multi-dimensional attributes as new itemsets into the sequences or rather subsequences, called MD-extension of the sequences.
In an advantageous aspect of the present disclosure multi-dimensional mining is based on a Seq-Dim algorithm. Every row in said sequence database can be represented by a multi-dimensional sequence which consists of two parts. The first part includes the dimensional information containing said multi-dimensional attributes. The second part is the sequence containing the event data. Thus, it can be of an advantageous effort to mine for sequential patterns at first and afterwards detect for frequent dimensional patterns.
Alternatively it might make more sense to go for a Dim-Seq algorithm detecting frequent dimensional attributes at first and then mining for sequential patterns in the corresponding sequences.
Another possibility for a pattern mining algorithm is the UniSeq algorithm. Therefore, it is mandatory to embed the additional multi-dimensional attributes into the sequence as new itemsets or rather eventsets, called MD-extension of the sequence. Thus, a sequential database is obtained which can be handled by a sequential mining algorithm, as UniSeq. UniSeq reduces the problem of mining multi-dimensional sequential patterns to mining sequential patterns with one additional itemset. Therefore, it is easy to implement. However, this method becomes inefficient when the number of dimensions increases.
The Seq-Dim algorithm can preferably comprise the steps of mining sequential patterns by a PrefixSpan algorithm firstly and detecting frequent dimensional attributes by a BUC-like algorithm (Bottom Up Computation) afterwards. A BUC algorithm is an efficient iceberg cube computing algorithm wherein said BUC algorithm is slightly amended to be suitable for detecting frequent dimensional attributes.
The adapted BUC-like algorithm may include the following steps:
taking the first dimension and order it alphabetically. Find all entries in this dimension that appear at least as often as minimum support demands wherein the minimum support stands for a certain threshold deciding whether a dimension is rated as a frequent one.
trying to grow these frequent dimensional attributes by taking the corresponding entries of the next dimension and scan for attributes appearing at least as often as minimum support.
By continuing said procedure all frequent dimensions can be detected which contain an item in the first dimension. After running this procedure with the first dimension, the algorithm is applied to the next dimension wherein the first dimension can be omitted in the further mining process. Recursively applying this procedure to every dimension, all frequent dimensions can be obtained.
Instead of mining for all patterns it can be more adequate to mine closed sequential patterns only, since they are the crucial part of all patterns. By means of an adapted version of the PrefixSpan algorithm closed sequential patterns can be detected.
In accordance to a further advantage of the present disclosure a ticket is automatically created in an issue tracking system in case of a matching pattern. Said ticket can be directed to a backend support team offering support for the occurred event. Additionally or alternatively, first diagnostic information or technical support might be offered automatically to the machine or rather the respective operating staff. The ticket may be a printed paper or a graphical representation supplied on a display device.
The use of multi-dimensional attributes also offers the opportunity to integrate non electronic parts reliability data in the logged event data of at least one machine. This enables the method to detect correlations between the found patterns and non electronic hardware failures of the machine.
In accordance with the present disclosure, the above-mentioned object is solved by a system comprising at least one machine, in particular a construction or hoisting machine, having data logging means for producing event data and a central processing unit for monitoring said at least one machine wherein said central unit is connected to a central pattern database. The central processing unit has means for receiving event data from at least one of the machines, means for mining a multi-dimensional sequential pattern within said received event data wherein at least one dimensional attribute holds information indicating said event data generating machine or at least one machine property, in particular the product family of the event data generating machine, and means for matching said mined multi-dimensional sequential pattern with patterns stored in a central pattern database.
The multi-dimensional attributes hold the information on the product family of the pattern generating machine. Mining multi-dimensional sequential patterns enables the central processor to detect patterns in a selection of machines and/or similar patterns within a product family and/or different product families having similar patterns.
At that point, the inventive system can monitor identical machines or differing machines of a single product family or similar product families. Moreover, by adding at least one attribute into the sequential patterns it is possible to describe the type and properties of machines supporting said sequential patterns. For example, the sequence patterns hold information about the event itself. Adding more attributes to the multi-dimensional patterns helps to provide additional information about the machine itself, for example at least one attribute describes the machine type, in particular a constructing or hoisting machine, at least one attribute identifies the membership to a special machine family, at least one attribute references the pattern to a special machine part, such as the machine drive, hydraulic system, mechanical parts, hoisting gear, etc.
Further, the system preferably comprises means for processing the above described inventive method. Obviously, the system shows the same advantages and properties as the inventive method.
Further, the present disclosure is also directed to a central processing unit for a system specified above which is suitable for performing the inventive method or a preferable example of said method.
Moreover, the present disclosure is directed to a computer usable medium having computer readable instructions stored thereon to be executed by a processor that performs the inventive method or an advantageous example of said inventive method.
Further details and advantages of the present disclosure will be explained in detail with reference to an example illustrated in the drawing.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.